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UVU-Store

shopify-graphql-mcp

by UVU-Store

create_order

Create new Shopify orders programmatically to import external orders or handle wholesale transactions.

Instructions

Create a new order programmatically. Useful for importing orders from external systems or creating wholesale orders. Requires write_orders scope.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoOrder note
tagsNoOrder tags
emailNoOrder email address
currencyNoCurrency code (e.g., 'USD', 'EUR')
lineItemsYesLine items for the order
customerIdNoExisting customer ID to associate with order (e.g., 'gid://shopify/Customer/123456789')
sendReceiptNoSend order receipt email to customer
discountCodeNoDiscount code to apply
customerEmailNoCustomer email (to create or upsert customer)
billingAddressNoBilling address (defaults to shipping if not provided)
financialStatusNoFinancial status of the order
shippingAddressNoShipping address
customerLastNameNoCustomer last name
customerFirstNameNoCustomer first name
fulfillmentStatusNoFulfillment status
sendFulfillmentReceiptNoSend fulfillment receipt email
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden for disclosure. It only mentions the required scope, but omits behavioral consequences such as whether payment is captured, receipts are sent, inventory is adjusted, or if the order is final and irreversible. This is a significant gap for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three short sentences, front-loaded with the primary purpose, then use cases, then scope. Every sentence earns its place with no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a complex tool (16 parameters, nested objects, no output schema, no annotations), but the description is extremely sparse. It fails to convey critical context such as the difference between create_order and create_draft_order, the financial impact of creating an order, or parameter interdependencies (e.g., variantId vs sku, customerEmail vs customerId).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with all 16 parameters well-described in the input schema. The tool description adds no parameter-level details, but per rubric, high schema coverage warrants a baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'create' and resource 'order', and provides use cases ('importing orders from external systems or creating wholesale orders'). However, it does not explicitly distinguish itself from sibling tools like create_draft_order, so it loses a point on sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives concrete context for when to use this tool (importing external orders, wholesale orders) and states a prerequisite (requires write_orders scope). It does not explicitly mention alternatives or when-not-to-use, but the use cases are clear enough to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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